Snowflake CoCo migrates Spark code to Snowflake in one prompt, delivering up to 5.1x speed gains

Snowflake's move to embed AI-assisted code migration within CoCo addresses a long-standing friction point for enterprises: moving workloads between data platforms. Spark is ubiquitous in data engineering but operationally expensive—clusters require constant tuning, patching, and infrastructure management. By offering a path to run existing Spark code on Snowflake's managed engine, the company removes that operational burden while claiming substantial performance and cost gains (up to 5.1x speed, 42% cost reduction). The key innovation is not the migration itself, but the depth of the AI agent's workflow: it scans for compatibility issues against a Snowflake-maintained knowledge base, applies targeted fixes in parallel, generates auditable reports, and optionally validates output correctness by running both versions against synthetic data. This end-to-end agentic approach—rather than a simple code rewriter—is designed to handle real-world complexity: RDD operations, UDF serialization patterns, unsupported file formats, and mixed-language codebases. The result is that what once required hours of manual effort and cross-file searching now runs in minutes from a single prompt, with the developer remaining in control of flagged items.